Search Results (141)

In recent years, the city of Naples has seen increasing attention focused on the mural dedicated to Diego Armando Maradona, located in the Spanish Quarter. Originally a spontaneous expression of street art and initially perceived by the local community as a simple artwork, the mural has gradually transformed into a powerful symbol of identity, becoming a true cultural icon of the city. This process has generated effects that transcend the artistic value of the artwork, contributing to the enhancement of the entire surrounding urban context and altering the social and economic dynamics of the quarter. By considering the Maradona Mural as the epicentre of a range of territorial and socio-economic effects, this paper aims to explore the issue of measuring the influence exerted by its presence on the market values of residential properties located in the Spanish Quarter of Naples. From this perspective, street art can be interpreted as a positive externality capable not only of fostering processes of urban regeneration and the redevelopment of formerly marginalised or degraded areas but also of influencing the dynamics of the local real estate market. Property values reflect not only the intrinsic characteristics of the properties but also the environmental and territorial attributes that contribute to the formation of market prices. From this perspective, properties can be considered as beneficiaries of externalities, whose economic value can be estimated through variations in house prices attributable to changes in environmental and territorial conditions. Full article

Urban regeneration has become a key strategy to address urban decline, infrastructure aging and socio-economic challenges, aiming to enhance quality of life and stimulate local development. Beyond physical renewal, it involves social, cultural and economic dimensions that contribute to the revitalization of communities. Recent literature frames regeneration as an adaptive and multifaceted process, drawing on concepts such as change management, dynamic capabilities and sensemaking to interpret urban transformation as a form of institutional learning. This study adopts a qualitative case study approach focused on the Libertà district in Bari (Italy), using document analysis, field observation, and stakeholder mapping to investigate both tangible and intangible outcomes of regeneration policies. Particular attention is devoted to understanding how socio-institutional and spatial transformations are translated into economic outcomes, using residential property values as an exploratory indicator of economic change. In this regard, variations in residential property values are considered as an exploratory indicator of economic dynamics associated with regeneration, while acknowledging that property price increases do not necessarily correspond to benefits for all community members and may also reflect issues related to affordability and social displacement. The findings suggest that regeneration should be understood as a coordinated and collaborative process involving multiple stakeholders and institutional actors. The analysis highlights the relevance of cultural initiatives, stakeholder interactions and changes in the physical environment as key elements of the transformation process. Furthermore, the study discusses the extent to which changes in the local real estate market may be associated with broader regeneration dynamics, recognizing the limitations of establishing direct causal relationships. Overall, the research proposes an original perspective on urban regeneration as a learning-based and organizational process in which social, institutional, and spatial transformations interact with economic change in complex and context-dependent ways. Full article

The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing two distinct temporal stages: the ante project phase (second half of 2021) and the in itinere phase (first half of 2025). Building on the findings of a previous pre-intervention study, the research systematically examines changes in market behaviors over time, with the dual purpose of identifying variations in price determinants and analyzing the associations between the current urban transformations and the residential real estate market. The results indicate a substantial stability in the main determinants of residential property prices across the two periods, suggesting that the regeneration initiative has not yet been fully capitalized into market behaviors. However, variations in the contribution and functional relationships of some spatial variables highlight preliminary signs of market adjustment during the ongoing transformation process. The study highlights the importance of monitoring for assessing how urban regeneration processes are progressively incorporated into real estate market dynamics. The proposed framework provides a transferable tool for evaluating regeneration processes in different urban contexts, supporting evidence-based decision-making and the comparative assessment of urban transformation strategies. Full article

Purpose: This study conducts a systematic literature review on the intersection of real estate exposure and geotechnical hazards, focusing specifically on seismic and subsurface risks. The objective is to synthesize key thematic trends, methodologies, and governance frameworks that inform risk-informed planning in seismically vulnerable urban areas. Design/methodology/approach: A Boolean search query was implemented on Lens.org, identifying 55 peer-reviewed articles published between January 2020 and May 2025. Inclusion criteria required explicit focus on property exposure to seismic or ground instability risks. Thematic analysis was conducted based on title and abstract data, supported by a Python (version 3.11)-generated word cloud to inductively identify five core clusters: (1) seismic assessment and earthquake risk, (2) building vulnerability and structural performance, (3) subsurface hazards and ground instability, (4) urban areas, heritage, and social vulnerability, and (5) risk mitigation, planning, and resilience frameworks. Findings: The review reveals a shift from hazard-centric, engineering-based models toward integrated, multi-scalar frameworks that embed risk within socio-economic, spatial, and institutional contexts. While consensus exists on the importance of probabilistic modeling, retrofitting, and GIS-based tools, divergences persist around behavioral valuation, policy uptake, and equity in implementation. Heritage cities and informal settlements emerge as under-addressed but critically vulnerable domains. Originality/value: This study systematically maps interdisciplinary research on real estate exposure to seismic and subsurface risks post-2020. By bridging engineering, planning, behavioral economics, and disaster governance, the review provides a unique synthesis relevant for academics, urban planners, and policymakers seeking to design equitable and resilient urban futures. The five-cluster thematic taxonomy introduced in this review represents an original synthesis that bridges engineering vulnerability assessment, behavioral economics, heritage preservation, and resilience governance. Unlike previous reviews that have typically focused on single disciplinary perspectives, this taxonomy integrates multi-scalar approaches spanning asset-level diagnostics to national exposure modeling, providing a comprehensive framework for understanding real estate exposure to seismic and subsurface risks. Full article

Traditional approaches to urban real estate green value assessment rely heavily on single structured data sources. Such methods often provide limited interpretability and fail to capture multidimensional green attributes accurately. To address these limitations, this study constructs a multimodal assessment framework that integrates image, text, and spatial information. A housing price prediction model is developed based on a Multi-Layer Perceptron architecture. Results show that the proposed method is superior to traditional models (such as the Hedonic pricing model, Ridge regression, and eXtreme Gradient Boosting, as well as single-modality control models). The core evaluation metric, mean squared error, reaches 0.0505 ± 0.0021. SHapley Additive exPlanations analysis shows that the text modality provides the largest contribution to model prediction, accounting for 51.45% of the global contribution. However, this dominance reflects the model’s dependence on textual green signals rather than the establishment of causal relationships. The result may also be influenced by marketing language bias and symbolic sustainability signals. The image modality contributes 38.48%, while the spatial modality contributes 10.07%, indicating a complementary relationship among the three modalities. Green premium analysis confirms that the model achieves higher prediction accuracy for high-priced residences and effectively captures differences in green premium across housing price tiers. This study provides a new technical pathway for real estate green value assessment. Full article

The present contribution provides a systematic review of the international scientific literature on the relationship between contaminated sites and real estate market dynamics. The objective is to investigate whether and to what extent the presence of environmental risk sources—both active or decommissioned—affects the value of surrounding residential properties. In particular, the review is focused on an examination of the methods commonly used in relevant studies to measure, interpret, and represent this impact across different geographical contexts, identifying the main magnitude ranges found in the selected contributions. Several studies consistently confirm a statistically significant negative relationship between proximity to polluting sites and real estate values, although the relevance of this effect varies considerably across case studies. Other records highlight non-notable impacts or even positive effects following remediation and redevelopment interventions. The evidence suggests that this relationship is complex and influenced by factors such as site type, contamination severity, specificities of the local urban context and community perception. Moreover, the findings underscore regional variations in the extent and nature of price impacts, reflecting diverse regulatory frameworks and remediation efforts. The outcomes of the literature review provide a robust foundation for developing more effective evaluation tools able to support decision-making processes, enabling policymakers, planners, and investors to promote sustainable urban regeneration, improve environmental justice, and reduce spatial inequalities. Ultimately, this study highlights the critical need for integrating environmental, social, and economic dimensions to fully capture the multifaceted effects of contaminated sites on property markets, thereby orienting more informed and equitable urban development strategies worldwide. Full article

Flooding poses a significant threat to commercial real estate investment, disrupting business operations, escalating maintenance costs, and heightening investment uncertainty, particularly in coastal and low-lying urban environments. This study examines the social dimensions of climate vulnerability by investigating how flood risk shapes stakeholders’ decisions to invest in commercial properties within flood-prone urban areas, with a focus on Lekki Phase 1, Lagos, Nigeria. A quantitative survey design was adopted. Data were collected from 87 commercial property investors through a structured questionnaire (FIIFRZQ) measured on a four-point Likert-type scale. The instrument demonstrated acceptable overall internal consistency (Cronbach’s α = 0.72), with subscale α values ranging from 0.62 to 0.81. Multiple regression analysis was used to assess the joint and individual contributions of seven factor categories (environmental, legal, economic, neighbourhood, structural, locational and behavioural) to investors’ willingness to invest in commercial property that is at risk of flooding. The seven predictors collectively explained 61.2% of the variance in investment willingness (R² = 0.612; F(7, 79) = 17.91; p < 0.001). Five factors, namely legal, environmental, structural, economic, and locational, were statistically significant contributors to investment willingness, while neighbourhood and behavioural factors were not. Johnson’s relative weights analysis confirmed legal and environmental considerations as the dominant drivers. The findings illuminate the interplay between climate vulnerability and investor behaviour in urban real estate markets, with actionable implications for policymakers, real estate practitioners, and investors navigating decision-making in flood-exposed urban environments. Full article

This study introduces RE-VAL (REal-estate VALuation), an explainable framework for urban real-estate analysis that integrates reproducible data acquisition, geographically informed feature processing, predictive benchmarking, and interpretable outputs suitable for decision-support-oriented analysis. Unlike static automated valuation models, the RE-VAL framework is designed to reflect context-dependent market behaviour across heterogeneous urban areas. The comparative evaluation on 1153 residential listings from Cagliari (Italy) showed that MLP achieved the strongest predictive performance, while Random Forest provided the most convincing balance between predictive competitiveness and interpretability. Beyond point estimation, the framework leverages SHAP-based decomposition to translate algorithmic outputs into transparent, monetary-based “Bonus/Malus” adjustment tables. The analysis highlights the presence of potentially non-linear interactions, including a possible premium associated with energy efficiency in prestigious areas, and suggests that the framework can remain informative when incomplete technical data are preserved as potential proxy signals rather than being discarded as noise. Rather than identifying a single predictor, RE-VAL provides a transparent, extensible and decision-oriented workflow for urban real-estate valuation, advancing the integration of explainable artificial intelligence within complex spatial-economic systems. Full article

This research examines how the relationship between cities and rural areas has evolved in light of the profound transformation affecting rural areas of high landscape value, which has been driven by the expansion opportunities granted to the real estate sector by urban planning regulations. The role of the landscape dimension in interpreting the relationship between territorial wealth and landscape value is considered, based on the convergence of two complementary disciplinary perspectives on territory: land planning and valuation science. Against this backdrop, and with a view to containing the progressive contamination of rural and agricultural heritage by the real estate sector, this study proposes a structured observation, valuation, interpretation, and regulatory tool to support the development of territorial planning in areas significantly characterized in terms of rural landscape value. The proposed tool is based on evidence regarding the phenomenon of building expansion in the agricultural territory of a municipality in southeastern Sicily, where favorable conditions for the development of the building sector exist, such as the vastness of the municipal territory and extensive farming as the mainstay of agricultural activity. This wider sub-regional area has also received attention due to the over-tourism phenomenon that has occurred in its cities of art. The evaluation approach experienced is a value-based representation of the evolution of this process over three observation periods: 2000, 2007, and 2012, relating the quantitative observation of the building expansion to the connected qualitative impact on rural landscape. It is the result of coordinating a large set of data in a hierarchical model of indices that converge to construct a synthetic index of rural landscape resilience. This achievement is based on the linguistic progression of “lexicon”, “semantics”, “syntax”, and “pragmatics”, each of which robustly supports “observation”, “valuation”, “interpretation”, and “planning”, respectively. The final stage is based on the convergence of explanatory indices, which are developed by coordinating evidence and assessments (factual and value judgements). This stage enables the proposal of a constraints system that supports a modus vivendi between the interests of the real estate sector and the values of the rural landscape in such a rich and fragile area. Full article

Due to the high population density and limited land availability in Bangkok, the capital of Thailand, land values have been increasing every year, posing challenges to sustainable housing development. Accurate land valuation is critical not only for investment decisions but also for promoting economic efficiency, social equity, and sustainable urban land use. Inaccurate analysis can lead to losses for real estate developers, project residents, and surrounding communities. However, this process requires extensive knowledge and experience. This research presents an approach for analyzing land values in Bangkok using Deep Learning techniques, which can help real estate developers assess appropriate land values more accurately and precisely. The study collected data on vacant land in Bangkok from an online feasibility study database and analyzed them using Deep Learning techniques. The results showed 30 determinants categorized into five groups. The study conducted 80 parameter adjustments with a ratio of 128:64:32 using a Quadratic Loss Function. The model was validated using k-fold cross-validation to ensure robustness and a Model Simulator operator to test sensitivity analysis. The Deep Learning model resulted in an R-square value of 0.917 and an RMSE of 2620 USD. The results of this research can be used as an effective decision-making tool for real estate developers, landowners, and brokers in determining appropriate buying or selling prices for land to support real estate sustainable development. Full article

The 15-min city (15 MC) is an urban planning concept that organizes cities through proximity-based systems, enabling residents to access essential services within a 15-min walk or cycle. Although the health and environmental benefits of this model are well documented, its effects on the real estate market have received limited attention. This paper examines the impact of 15-min proximity-based urban planning models on land use patterns, property values, and sociospatial interactions in urban settings. It adopts a scoping review approach (structured mapping and synthesis of the available literature) and, using a transparent source selection process (PRISMA-ScR), compiles evidence on how functional accessibility, mixed uses, and proximity to green/public spaces affect prices and rents in residential and/or commercial real estate. The synthesis shows that proximity is often capitalized as a proximity premium, but it can exacerbate inequalities and displacement risks without accompanying regulatory mechanisms. Based on the findings, an operational spatial–economic framework is proposed that links (a) planning interventions, (b) functional accessibility, (c) behavioral adaptation, (d) market valuation reactions, and (e) governance/redistribution tools (e.g., land value capture, inclusionary zoning), as a diagnostic tool for assessing surplus value and displacement risk and as a basis for future GIS/hedonic testing. Full article

The loss of urban nature and declining biodiversity pose significant challenges to the sustainability of cities and the well-being of their inhabitants. Existing initiatives such as the Taskforce on Nature-related Financial Disclosures (TNFD) have begun to address ecological risks in real estate, but they still address mental health, biodiversity, and social equity only partially as non-financial values. This article adopts an integrative review and conceptual framework approach. It develops a nature-based accounting framework for urban real estate that combines principles of traditional Chinese architecture with contemporary sustainability metrics. The study reviews ecological theory, nature-related accounting, and evidence on biodiversity and mental health, and then undertakes an operational mapping from classical site planning, courtyard design, water management, and community structures to measurable indicators that remain compatible with TNFD-aligned reporting. The framework groups indicators into three main domains: nature-related conditions, ecosystem service pathways, and human well-being outcomes. It also outlines simple procedures for normalising and combining these indicators at the project scale to support assessments of biodiversity, microclimate, mental health, and basic aspects of cost-effectiveness and social accessibility in urban real estate projects. The paper provides a structured, heritage-informed basis for future applications and empirical testing, helping to incorporate biodiversity, mental health, and equity into urban real estate assessment. Full article

The intangible accessibility of real estate markets via platforms like Airbnb profoundly influences the urban development industry, propelled by the dynamics of short- to medium-term rentals for tourists. The suggested study aims to examine the association between the prices of listed properties and the influence of proximity to tourist attractions on location-driven pricing. The city of Rome acts as a case study from which to derive pertinent conclusions and proof on the phenomena intended for exploration. The methodological approach relies on a comprehensive classification of locations recognized as tourist attractions, drawn from public resources, travel guides, search engines, and online trends. The identified attractionswere subsequently classified to analyze how spatial proximity influences price formation. Data on short-term rental listings were obtained from the Inside Airbnb platform. The results enable the characterization of Rome as a polycentric urban system, composed of multiple tourism hubs whose spatial interactions are closely associated with prevailing hotel pricing patterns. This study emphasizes the influence of tourist demand on land values, a phenomenon intricately connected to urban gentrification and the capitalization of the real estate market. These findings enhance comprehension of tourism’s impact on the geographical and economic structure of cities. Full article

Clarifying the residential space differentiation in urban fringe areas and its influencing factors are crucial for land use planning and sustainable urban development. This study investigates residential space differentiation and its influencing factors in the urban fringe area of Beijing from the perspective of housing rent. Utilizing multi-source data, including housing rent statistics from the China Real Estate Price Platform, remote sensing imagery, and POI big data, we employ the residential dissimilarity index for tenants, geographical detector, and MGWR model to analyze spatial patterns and driving mechanisms. The results show the following: (1) The residential space differentiation in the urban fringe area of Beijing is obvious, showing an “X”-shaped fragmentation pattern, with the northeast and southwest regions forming high differentiation values, while the northwest and southeast regions form low differentiation values. (2) The residential space differentiation in the marginal area shows a strong scale effect, which originates from the historic “collage” development mode of Beijing. (3) The differentiation of residential space in Beijing’s urban fringe area is sensitive to the spatial accessibility of residential areas to other facilities, and is less affected by the spatial proximity, such as the number of facilities. (4) The central potential and traffic potential factors are still the core driving forces shaping the differentiation pattern of residential space in the marginal area; the role of leisure supporting factors has become increasingly prominent, and it has gradually become the key factor strengthening residential space differentiation; and the influence of medical and commercial supporting factors is relatively weak. Full article

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development. Full article